The invention relates to image analysis and, more specifically, to a computer implemented method and system for the processing of digitized images to automatically detect structures of interest therein. In one application, the invention comprises a method and apparatus for detecting microcalcification clusters in mammograms.
Identification of small and low-contrast structures in images requires methods and systems for characterizing these structures and separating them from the background. One example requiring such methods and systems is the detection of microcalcifications in mammograms, indicating the possibility of a malignant tumor.
Breast cancer, by far the leading type of cancer incidence in women, causes about 170,000 new cases a year, more than double the amount caused by colorectal cancer, the second major type in women. However, early diagnosis and treatment of breast cancer provide one of the highest chances of survival among cancer types in women. The American Cancer Society recommends a yearly mammogram examination for asymptomatic women over the age of 35 and Medicare covers these procedures.
Awareness and willingness for prevention of breast cancer is rapidly increasing in the general public. Therefore, it is possible that mammography will soon be one of the highest volume X-ray procedures regularly used in radiology clinics. The increasing burden on radiologists is being experienced at many medical centers. A reliable computerized method and system can contribute both speed and accuracy to mammogram interpretation.
The first and sometimes the only mammographic sign in early, curable breast cancer is a cluster of microcalcifications that are visible in about 50% of breast cancer cases. Microcalcifications typically have a higher X-ray opacity than that of normal breast tissue and they appear as relatively brighter structures ranging from 0.1 mm to 2 mm in width in a mammogram. In visual inspection, one cluster of microcalcifications consists of 3 or more individual microcalcifications that appear in an area of about 1 cm2.
Due to the subtlety of some microcalcifications, visual interpretation of a mammogram is a tedious process that generally requires a magnifying glass, and that, in some cases, can take more than 15 minutes. In visual inspection, the probability of false negatives is high and a significant level of false positives is reported, i.e., only one out of five cases that radiologists interpret as potential cancer is confirmed in a biopsy examination.
The factors that contribute to the difficulty of visually recognizing microcalcifications are their small size; their morphological variability; their similarity to other microstructures that are unrelated to cancer, e.g., film artifacts, lead shot positioning markers, and some benign tissue structures; and the relatively low contrast of mammograms.
For an automated, computerized method and system, the small size of microcalcifications does not pose a large problem because digitization resolutions (e.g., 25 microns/pixel) that provide adequate information on the smallest microcalcifications are available. However, the other three factors present challenges that successful automated systems have to meet.
Previously developed automated detection techniques reported varying levels of performance with different methods. See, H-P. Chan et al., xe2x80x9cComputer-aided detection of microcalcifications in mammograms: methodology and preliminary clinical study,xe2x80x9d Invest. Radiol., vol. 23, p. 664, 1988; B. W. Fam et al., xe2x80x9cAlgorithm for the detection of fine clustered calcifications on film mammograms,xe2x80x9d Radiology, vol., 169, p. 333, 1988; and D. H. Davies and D. R. Dance, xe2x80x9cAutomatic computer detection of clustered calcifications in digital mammograms,xe2x80x9d Phys. Med. Biol., vol. 35, p. 1111, 1990.
The potential difficulties and pitfalls of many automated detection techniques can be summarized as follows:
a. Too little enhancement may preclude the detection of minor microcalcification peaks while too much enhancement may increase significantly the amplitude of small background structures (noise) and thus produce a large number of false detections. An acceptable compromise may not exist in some images, and in those images where it exists, it can change from image to image and can be difficult to determine.
b. A small, square region of analysis (moving kernel) where operational parameters are computed, may be inappropriate for the natural shape of microcalcifications and automated detection based on such approaches may depart considerably, in some cases, from the outcome of visual detection.
c. A large number of parameters whose values have to be entered manually (e.g., Fam) is not a viable approach for expedient clinical use.
Considering the limitations discussed above, any new detection method and apparatus has to meet the following requirements:
a. Operate on raw data (no enhancement) to ensure that both visual interpretation and automated detection use the same information.
b. Have an approach that is compatible with the natural morphology of microcalcifications, i.e., no use of small square areas of interest or moving kernels.
c. Require a minimal number of operational parameters that can be set adaptively and automatically for any image, allowing fully automated operation.
d. Allow for visual interpretability of operational parameters.
The above considerations, and the unsatisfactory results obtained with some of the available detection techniques, has led to the development of the fundamentally different detection method and apparatus described and claimed herein.
In generalized form, the invention is an image analysis method and apparatus implemented in a computer for automatically detecting small structures in images. The specific embodiment described below is useful as a diagnostic aid that determines, on a digitized mammogram, the location of clusters of microcalcifications whose morphological properties are similar to those observed in malignant microcalcification clusters confirmed by biopsy.
The invention first digitizes the mammogram and preprocesses the digitized image through a nonlinear filter that eliminates very high frequency noise. The resultant image is then stored in the form of a matrix in the memory of a data processing means/computer.
In the computer, the digitized image is segmented into candidate structures by first locating seed pixels, defined as pixels which are brighter than their immediate neighbors. Each seed pixel in the image is used as the basis for constructing/growing two regions. In the first region, adjacent pixels are added to the region if: (1) they have a gradient value higher than the pixels they adjoin (touch) in the grown region and (2) they have a gray level lower than the pixels they adjoin in the grown region. The second region is constructed/grown by adding adjacent pixels if they have a gray level lower than the pixels they adjoin in the grown region.
Following construction of the two regions, various features are measured using the two regions around each seed point. Certain of the features, taken together, characterize each candidate structure and are used as input to a classifier, such as a neural network. The classifier will then distinguish the candidate structures between structures of interest and background. The structures detected by the classifier will then be presented to a clustering algorithm. A detected structure that is less than a threshold distance from the nearest structure in a cluster is included in that cluster. Finally, the results are displayed either on a monitor or on hard copy with a frame around the detected cluster.